This study uses a regime sorting technique to explore the relationships that ACCESS1.3 clouds have with the large-scale environment. Satellite simulator output is used to demonstrate that the modeled clouds have similar sensitivity to the large-scale dynamic and thermodynamic conditions as shown by CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). The high cloud cover and longwave cloud radiative effect is represented very well in the model across all regimes. The cloud types that the model simulates the most poorly are stratocumulus over cool sea surface temperatures (SSTs) and the deep convective regimes associated with strong upward midtropospheric vertical velocity and weak lower tropospheric stabilities. The reflectance of the deep convective regimes shows a stronger sensitivity to SST and less dependence on the large-scale dynamics than the observations. Many of the model errors identified occur across all regimes, such as the underestimate of clouds with large scattering ratios (SR) and the too frequent occurrence of drizzle and rain. A sensitivity test in which a different warm rain scheme was used shows that the modelled frequency of occurrence of nonprecipitating low cloud is quite sensitive to the autoconversion parameterization. The new scheme produced more cloud with large SR and higher cloud tops in better agreement with the observations. The thermodynamic regime analysis shows that the transition of shallow to deeper convection in the model requires a warmer SST and weaker LTS than the observations. The significant underestimate of cumulus congestus is likely to contribute to this delay due to the role these clouds have in preconditioning the midtroposphere for the onset of deep convection.